Speech-based identification of suicide risk in patients with MDD: model development and 4-week follow-up validation

Background

Patients with major depressive disorder (MDD) have a substantially higher risk of suicide than the general population, whereas current suicide risk identification mainly relies on clinical interviews and standardized rating scales, with limited objectivity and capacity for continuous monitoring. Speech is a low-burden and repeatable digital behavioral signal that can provide acoustic and linguistic information related to emotional state, cognitive processing, and verbal expression, and may offer an objective adjunct for suicide risk identification in patients with MDD. Accordingly, this study aimed to evaluate whether speech-derived features could support suicide risk identification in patients with MDD by comparing the discriminative performance of different speech representations, examining the added value of clinical symptom measures, and assessing model performance at a 4-week follow-up.